Data science workflow

Grolemund and Wickham, http://r4ds.had.co.nz/introduction.html

Different purposes

…exploratory…

…analysis and diagnosis…

…presentation…

Comprehend this:

data(anscombe)
anscombe[ , c(1,5,2,6,3,7,4,8)]

##    x1    y1 x2   y2 x3    y3 x4    y4
## 1  10  8.04 10 9.14 10  7.46  8  6.58
## 2   8  6.95  8 8.14  8  6.77  8  5.76
## 3  13  7.58 13 8.74 13 12.74  8  7.71
## 4   9  8.81  9 8.77  9  7.11  8  8.84
## 5  11  8.33 11 9.26 11  7.81  8  8.47
## 6  14  9.96 14 8.10 14  8.84  8  7.04
## 7   6  7.24  6 6.13  6  6.08  8  5.25
## 8   4  4.26  4 3.10  4  5.39 19 12.50
## 9  12 10.84 12 9.13 12  8.15  8  5.56
## 10  7  4.82  7 7.26  7  6.42  8  7.91
## 11  5  5.68  5 4.74  5  5.73  8  6.89

compared to:

Put the data in its place

Use during analysis

present results

Illustrate concepts

Illustrate concepts

Principles

• well-designed presentation of interesting data - substance, statistics, and design
• complex ideas communicated with clarity, precision, and efficiency
• greatest number of ideas in the shortest time with the least ink in the smallest space
• nearly always multivariate
• telling the truth about the data

Adapted from Tufte

Cluttered

Minimal axis guides

Fade axis title

Remove borders

Remove boxes

Guidelines to back

Background to back

Consistent doc theme

Consistent font

Corporate colours

Direct labels

Much better than:

Original

User-friendly labels

Horizontal text

Meaningful ordering

Better shape and geom

Labels on points

Title and annotation

Another dimension

Better than:

Difficult

Use cartesian coordinates

Use height

Flip for readability

Sequence

Maximise focus on data

Labels near the data

Use like a table

Better than

Not just this

But this

Or this

Key points in common:

• Comparative
• Multivariate
• High data density
• Reveal interactions and comparisons
• Nearly all the ink is data ink